ABSTRACT
This paper introduces a Machine learning intrusion detection system (IDS) to detect DoS attacks and FUZZY attacks on CAN bus in smart vehicles and classify messages to Normal, DoS, or FUZZY. The aim of using the machine learning techniques with optimizers is to improve the performance of intrusion detection system. Our intrusion detection scheme was performed using an open source real dataset CAN-intrusion-dataset. When we accomplished the preparation of proposed scheme, the dataset was divided to four sub datasets with four experiments and the datasets were cleaned and preprocessed using the Weka tool and MATLAB Data Cleaner tool box. The proposed detection scheme achieved a 97.73% for DT (decision tree) and 99.15% DT with Bayesian optimizer 99.15% DT with grid search 99.15% DT with random search and 98.42% with KNN accuracy rate, the results of using optimizer with machine learning techniques obtained from the proposed detection scheme were compared with other recent literature results. The findings indicate that this model is more accurate than other methods.
- Ullah, S.; Khan, M.A.; Ahmad, J.; Jamal, S.S.; e Huma, Z.; Hassan, M.T.; Pitropakis, N.; Arshad; Buchanan, W.J. HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles. Sensors 2022, 22, 1340Google Scholar
- Wright, S. Autonomous cars generate more than 300 TB of data per year. 2021. Available online: https://www.tuxera.com/blog/autonomous-cars-300-tb-of-data-per-year/Google Scholar
- How Many Cars Are There in the World? Available online: https://www.carsguide.com.au/car-advice/how-many-cars-are-there-in-the-world-70629, Accessed 31 December 2022Google Scholar
- Sani, A. R., Hassan, M. U., & Chen, J. (2022). Privacy Preserving Machine Learning for Electric Vehicles: A Survey. arXiv preprint arXiv:2205.08462Google Scholar
- Moulahi, T., Zidi, S., Alabdulatif, A., & Atiquzzaman, M. (2021). Comparative performance evaluation of intrusion detection based on machine learning in in-vehicle controller area network bus. IEEE Access, 9, 99595-99605Google ScholarCross Ref
- Alsarhan, A., Al-Ghuwairi, A. R., Almalkawi, I. T., Alauthman, M., & Al-Dubai, A. (2021). Machine learning-driven optimization for intrusion detection in smart vehicular networks. Wireless Personal Communications, 117(4), 3129-3152Google ScholarDigital Library
- Banafshehvaragh, S. T., & Rahmani, A. M. (2023). Intrusion, anomaly, and attack detection in smart vehicles. Microprocessors and Microsystems, 96, 104726Google ScholarDigital Library
- Golson, J. Jeep Hackers at It Again, This Time Taking Control of Steering and Braking Systems. 2 August 2016. Available online: https://www.theverge.com/2016/8/2/12353186/car-hack-jeep-cherokee-vulnerability-miller-valasek, Accessed 31 December 2022Google Scholar
- Alsarhan, A., Alauthman, M., Alshdaifat, E. A., Al-Ghuwairi, A. R., & Al-Dubai, A. (2023). Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks. Journal of Ambient Intelligence and Humanized Computing, 14(5), 6113-6122Google ScholarCross Ref
- Lee, H., Jeong, S. H., & Kim, H. K. (2017, August). OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In 2017 15th Annual Conference on Privacy, Security and Trust (PST) (pp. 57-5709). IEEEGoogle ScholarCross Ref
- Ahmed, I., Ahmad, A., & Jeon, G. (2021). Deep Learning-based Intrusion Detection System for Internet of Vehicles. IEEE Consumer Electronics MagazineGoogle Scholar
- Ali, E. S., Hasan, M. K., Hassan, R., Saeed, R. A., Hassan, M. B., Islam, S., ... & Bevinakoppa, S. (2021). Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications. Security and Communication Networks, 2021Google ScholarDigital Library
- Boland, H. M., Burgett, M. I., Etienne, A. J., & Stwalley III, R. M. (2021). An Overview of CAN-BUS Development, Utilization, and Future Potential in Serial Network Messaging for Off-Road Mobile Equipment. Technology in Agriculture.Google Scholar
- Decision Trees, classification learner, Mathworks https://www.mathworks.com/help/stats/decision-trees.html, Accessed 31 December 2022Google Scholar
- Matlab documentation, Statistics and Machine Learning Toolbox, Classifications, Model Building and Assessment, Bayesian Optimization Workflow, https://www.mathworks.com/help/stats/bayesian-optimization-algorithm.html, Accessed 31 December 2022Google Scholar
- Matlab documentation, Statistics and Machine Learning Toolbox, classifications, Model , Hyper parameter Optimization , Optimization Options https://www.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html#mw_7a976371-1a5c-4c27-a258-563c9883a255 , Accessed 31 December 2022Google Scholar
- Matlab documentation, Statistics and Machine Learning Toolbox, Classifications, Nearest Neighbors, Classification KNN https://www.mathworks.com/help/stats/classificationknn.html, Accessed 31 December 2022Google Scholar
- Avatefipour, Omid, "An intelligent secured framework for cyberattack detection in electric vehicles’ CAN bus using machine learning." IEEE Access 7 (2019): 127580-127592Google Scholar
- Fiorese, Tobia, and Pietro Montino. "Learning-based Intrusion Detection System for On-Board Vehicle Communication." ITASEC. 2021Google Scholar
- Azzaoui, N., Korichi, A., Brik, B., & Fekair, M. E. A. (2021). Towards optimal dissemination of emergency messages in Internet of Vehicles: A dynamic clustering-based approach. Electronics, 10(8), 979.Google ScholarCross Ref
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